Part_1_notebook
library(googleAnalyticsR)
library(keyring)
library(ggplot2)
library(tidyverse)
library(lubridate)
library(ggpubr)#Get data for FY2017-2018 for sessions and goal completions by date
seventeen_eighteen_app_values <- read_csv("../inputs/seventeen_eighteen_apps.csv")Parsed with column specification:
cols(
date = [34mcol_date(format = "")[39m,
month = [31mcol_character()[39m,
year = [32mcol_double()[39m,
dayofWeekName = [31mcol_character()[39m,
day = [31mcol_character()[39m,
goal9Completions = [32mcol_double()[39m,
sessions = [32mcol_double()[39m,
goal3Completions = [32mcol_double()[39m,
goal5Completions = [32mcol_double()[39m,
goal12Completions = [32mcol_double()[39m
)summary(seventeen_eighteen_app_values)#Plot application values against month
ggplot(seventeen_eighteen_app_values) +
aes(x = month, y = goal9Completions) +
geom_col() +
geom_hline(yintercept = 45, colour = "red")
#Plot app values against day of week
ggplot(seventeen_eighteen_app_values) +
aes(x = dayofWeekName, y = goal9Completions) +
geom_col() #Plot app values and sessions over entire year
app_seventeen_plot <- ggplot() +
geom_line(data = seventeen_eighteen_app_values,
aes(x = date, y = goal9Completions)) +
labs(x = "Date", y = "Applications")
sessions_seventeen_plot <- ggplot() +
geom_line(data = seventeen_eighteen_app_values,
aes(x = date, y = sessions)) +
labs(x = "Date", y = "Sessions")
ggarrange(sessions_seventeen_plot, app_seventeen_plot,
ncol = 1, nrow = 2)
ggsave("seventeen_sessions.jpg")Saving 7.29 x 4.51 in image
#Plot app values and info session clicks over entire year
goal3_seventeen <- ggplot() +
geom_line(data = seventeen_eighteen_app_values,
aes(x = date, y = goal3Completions, colour = "blue")) +
geom_line(data = seventeen_eighteen_app_values,
aes(x = date, y = goal5Completions, colour = "red")) +
labs(x = "Date", y = "Info Session Clicks") +
theme(legend.position = "none")
goal9_seventeen<-
ggplot()+
geom_line(data = seventeen_eighteen_app_values,
aes(x = date, y = goal9Completions)) +
labs(x = "Date", y = "Applications")
ggarrange(goal3_seventeen, goal9_seventeen,
ncol = 1, nrow = 2)
ggsave("seventeen_goal35.jpg")Saving 7.29 x 4.51 in image
#Plot app values and info packs over entire year
info_packs_seventeen_plot <- ggplot() +
geom_line(data = seventeen_eighteen_app_values,
aes(x = date, y = goal12Completions, colour = "blue")) +
labs(x = "Date", y = "Info Pack Requests") +
theme(legend.position = "none")
ggarrange(info_packs_seventeen_plot, goal9_seventeen,
ncol = 1, nrow = 2)
ggsave("seventeen_goal12.jpg")Saving 7.29 x 4.51 in image
#Get correlations between metrics and app values
cor(seventeen_eighteen_app_values$goal9Completions, seventeen_eighteen_app_values$sessions)[1] 0.4036846cor(seventeen_eighteen_app_values$goal9Completions, seventeen_eighteen_app_values$goal3Completions)[1] 0.2278833cor(seventeen_eighteen_app_values$goal9Completions, seventeen_eighteen_app_values$goal5Completions)[1] 0.07772192cor(seventeen_eighteen_app_values$goal9Completions, seventeen_eighteen_app_values$goal12Completions)[1] 0.03673292Strongest correlation between sessions and app values but weak - info packs not really correlated, too weak
#Get data for FY2018/2019
eighteen_nineteen_app_values <- read_csv("../inputs/eighteen_nineteen_apps.csv")Parsed with column specification:
cols(
date = [34mcol_date(format = "")[39m,
month = [31mcol_character()[39m,
year = [32mcol_double()[39m,
dayofWeekName = [31mcol_character()[39m,
day = [31mcol_character()[39m,
goal9Completions = [32mcol_double()[39m,
sessions = [32mcol_double()[39m,
goal3Completions = [32mcol_double()[39m,
goal5Completions = [32mcol_double()[39m,
goal12Completions = [32mcol_double()[39m
)summary(eighteen_nineteen_app_values) date month year dayofWeekName
Min. :2018-04-01 Length:365 Min. :2018 Length:365
1st Qu.:2018-07-01 Class :character 1st Qu.:2018 Class :character
Median :2018-09-30 Mode :character Median :2018 Mode :character
Mean :2018-09-30 Mean :2018
3rd Qu.:2018-12-30 3rd Qu.:2018
Max. :2019-03-31 Max. :2019
day goal9Completions sessions goal3Completions goal5Completions
Length:365 Min. :0.000 Min. : 90.0 Min. : 0.000 Min. :0.00
Class :character 1st Qu.:0.000 1st Qu.:227.0 1st Qu.: 0.000 1st Qu.:0.00
Mode :character Median :1.000 Median :314.0 Median : 1.000 Median :2.00
Mean :1.115 Mean :305.4 Mean : 1.756 Mean :2.06
3rd Qu.:2.000 3rd Qu.:371.0 3rd Qu.: 3.000 3rd Qu.:3.00
Max. :8.000 Max. :697.0 Max. :15.000 Max. :9.00
goal12Completions
Min. :0.000
1st Qu.:1.000
Median :2.000
Mean :2.233
3rd Qu.:3.000
Max. :8.000 #Create data frame with both year's data
all_years_app_values <-
rbind(seventeen_eighteen_app_values, eighteen_nineteen_app_values)
all_years_app_values <-
all_years_app_values %>%
mutate(FY = case_when(
month %in% c("04", "05", "06", "07", "08", "09", "10", "11", "12") & year == "2017" ~ "2017/18",
month %in% c("01", "02", "03") & year == "2018" ~ "2017/18",
month %in% c("04", "05", "06", "07", "08", "09", "10", "11", "12") & year == "2018" ~ "2018/19",
month %in% c("01", "02", "03") & year == "2019" ~ "2018/19",
))sum(all_years_app_values$goal9Completions)[1] 919#Plot app values for each month and compare to last year
all_years_app_values %>%
group_by(month, FY) %>%
summarise(total_apps = sum(goal9Completions)) %>%
ggplot() +
geom_col(aes(x = month, y = total_apps, fill = FY), position = "dodge") +
scale_fill_manual(values = c("violetred3", "springgreen3")) +
geom_hline(yintercept = 45) +
labs(title = "Applications per Month", x = "Month", y = "Number of Applications") +
theme(title = element_text(size = 12, face = "bold"),
axis.title = element_text(size = 12, face = "bold"),
axis.text = element_text(size = 10),
legend.title = element_text(size = 10))
ggsave("apps_month.jpg")Saving 7.29 x 4.51 in image
#Plot apps by day of the week and compare to last year
all_years_app_values %>%
group_by(dayofWeekName, FY) %>%
summarise(total_apps = sum(goal9Completions)) %>%
ggplot() +
geom_col(aes(x = reorder(dayofWeekName, total_apps), y = total_apps, fill = FY), position = "dodge") +
scale_fill_manual(values = c("violetred3", "springgreen3")) +
labs(title = "Applications per Week Day", x = "Day", y = "Number of Applications") +
theme(title = element_text(size = 12, face = "bold"),
axis.title = element_text(size = 12, face = "bold"),
axis.text = element_text(size = 10),
legend.title = element_text(size = 10))
ggsave("apps_day.jpg")Saving 7.29 x 4.51 in image
sum(seventeen_eighteen_app_values$goal9Completions)[1] 512sum(eighteen_nineteen_app_values$goal9Completions)[1] 407#Plot apps by day of the month and compare to last year
all_years_app_values %>%
group_by(day, FY) %>%
summarise(total_apps = sum(goal9Completions)) %>%
ggplot() +
geom_col(aes(x = day, y = total_apps, fill = FY), position = "dodge") +
scale_fill_manual(values = c("violetred3", "springgreen3")) +
labs(title = "Applications per Month Day", x = "Day of Month", y = "Number of Applications") +
theme(title = element_text(size = 12, face = "bold"),
axis.title = element_text(size = 12, face = "bold"),
axis.text = element_text(size = 10),
legend.title = element_text(size = 10))
ggsave("apps_month_day.jpg")Saving 7.29 x 4.51 in image
#Plot sessions vs apps for FY2018/19
app_eighteen_plot <- ggplot() +
geom_line(data = eighteen_nineteen_app_values,
aes(x = date, y = goal9Completions)) +
labs(x = "Date", y = "Applications")
sessions_eighteen_plot <- ggplot() +
geom_line(data = eighteen_nineteen_app_values,
aes(x = date, y = sessions)) +
labs(x = "Date", y = "Sessions")
ggarrange(sessions_eighteen_plot, app_eighteen_plot,
ncol = 1, nrow = 2)
ggsave("eighteen_sessions.jpg")Saving 7.29 x 4.51 in image
#Plot app values and info session clicks over entire year
info_clicks_eighteen_plot <- ggplot() +
geom_line(data = eighteen_nineteen_app_values,
aes(x = date, y = goal3Completions, colour = "blue")) +
geom_line(data = eighteen_nineteen_app_values,
aes(x = date, y = goal5Completions, colour = "red")) +
labs(x = "Date", y = "Info Session Clicks") +
theme(legend.position = "none")
goal9_eighteen_plot <-
ggplot() +
geom_line(data = eighteen_nineteen_app_values,
aes(x = date, y = goal9Completions)) +
labs(x = "Date", y = "Applications")
ggarrange(info_clicks_eighteen_plot, goal9_eighteen_plot,
ncol = 1, nrow = 2)
ggsave("eighteen_goal35.jpg")Saving 7.29 x 4.51 in image
#Plot app values and info packs over entire year
info_packs_eighteen_plot <- ggplot() +
geom_line(data = eighteen_nineteen_app_values,
aes(x = date, y = goal12Completions, colour = "red")) +
labs(x = "Date", y = "Info Pack Requests") +
theme(legend.position = "none")
ggarrange(info_packs_eighteen_plot, goal9_eighteen_plot,
ncol = 1, nrow = 2)
ggsave("eighteen_goal12.jpg")Saving 7.29 x 4.51 in image
#Get correlations between metrics and app values
cor(eighteen_nineteen_app_values$goal9Completions, eighteen_nineteen_app_values$sessions)[1] 0.3057772cor(eighteen_nineteen_app_values$goal9Completions, eighteen_nineteen_app_values$goal3Completions)[1] 0.1224082cor(eighteen_nineteen_app_values$goal9Completions, eighteen_nineteen_app_values$goal5Completions)[1] 0.3110095cor(eighteen_nineteen_app_values$goal9Completions, eighteen_nineteen_app_values$goal12Completions)[1] 0.1509175#Compare applications for each year
ggplot() +
geom_line(data = seventeen_eighteen_app_values, aes(x = date, y = goal9Completions), colour = "violetred3") +
geom_line(data = eighteen_nineteen_app_values, aes(x = date, y = goal9Completions), colour = "springgreen3") +
labs(x = "Date", y = "Number of Applications") +
theme(title = element_text(size = 12, face = "bold"),
axis.title = element_text(size = 12, face = "bold"),
axis.text = element_text(size = 10),
legend.title = element_text(size = 10))
ggsave("apps_2years.jpg")Saving 7.29 x 4.51 in image
#Compare sessions for each year
ggplot() +
geom_line(data = seventeen_eighteen_app_values, aes(x = date, y = sessions), colour = "violetred3") +
geom_line(data = eighteen_nineteen_app_values, aes(x = date, y = sessions), colour = "springgreen3") +
labs(x = "Date", y = "Number of Sessions") +
theme(title = element_text(size = 12, face = "bold"),
axis.title = element_text(size = 12, face = "bold"),
axis.text = element_text(size = 10),
legend.title = element_text(size = 10))
ggsave("session_2years.jpg")Saving 7.29 x 4.51 in image
#Compare info clicks for each year
ggplot() +
geom_line(data = seventeen_eighteen_app_values, aes(x = date, y = goal3Completions), colour = "red") +
geom_line(data = seventeen_eighteen_app_values, aes(x = date, y = goal5Completions), colour = "blue") +
geom_line(data = eighteen_nineteen_app_values, aes(x = date, y = goal3Completions), colour = "yellow") +
geom_line(data = eighteen_nineteen_app_values, aes(x = date, y = goal5Completions), colour = "green") +
labs(x = "Date", y = "Number of Info Session Clicks") +
theme(title = element_text(size = 12, face = "bold"),
axis.title = element_text(size = 12, face = "bold"),
axis.text = element_text(size = 10),
legend.title = element_text(size = 10))
ggsave("goal35_2years.jpg")Saving 7.29 x 4.51 in image
#Compare info packs by year
ggplot() +
geom_line(data = seventeen_eighteen_app_values, aes(x = date, y = goal12Completions), colour = "violetred3") +
geom_line(data = eighteen_nineteen_app_values, aes(x = date, y = goal12Completions), colour = "springgreen3") +
labs(x = "Date", y = "Number of Info Pack Requests") +
theme(title = element_text(size = 12, face = "bold"),
axis.title = element_text(size = 12, face = "bold"),
axis.text = element_text(size = 10),
legend.title = element_text(size = 10))
ggsave("goal12_2years.jpg")Saving 7.29 x 4.51 in image
#Plot apps for 2017/18
all_years_app_values %>%
filter(FY == "2017/18") %>%
ggplot() +
aes(x = date, y = goal9Completions) +
geom_line()
#Plot apps for 2018/19
all_years_app_values %>%
filter(FY == "2018/19") %>%
ggplot() +
aes(x = date, y = goal9Completions) +
geom_line()
---
title: "Part_1_notebook"
output: html_notebook
---

```{r}
library(googleAnalyticsR)
library(keyring)
library(ggplot2)
library(tidyverse)
library(lubridate)
library(ggpubr)
```

```{r}
#Get data for FY2017-2018 for sessions and goal completions by date
seventeen_eighteen_app_values <- read_csv("../inputs/seventeen_eighteen_apps.csv")
```

```{r}
summary(seventeen_eighteen_app_values)
```

```{r}
#Plot application values against month 
ggplot(seventeen_eighteen_app_values) +
  aes(x = month, y = goal9Completions) +
  geom_col() +
  geom_hline(yintercept = 45, colour = "red")
```

```{r}
#Plot app values against day of week
ggplot(seventeen_eighteen_app_values) +
  aes(x = dayofWeekName, y = goal9Completions) +
  geom_col() 
```

```{r}
#Plot app values  and sessions over entire year
app_seventeen_plot <- ggplot() +
  geom_line(data = seventeen_eighteen_app_values, 
           aes(x = date, y = goal9Completions)) +
  labs(x = "Date", y = "Applications")

sessions_seventeen_plot <- ggplot() +
  geom_line(data = seventeen_eighteen_app_values, 
           aes(x = date, y = sessions)) +
  labs(x = "Date", y = "Sessions")

ggarrange(sessions_seventeen_plot, app_seventeen_plot,
          ncol = 1, nrow = 2)
ggsave("seventeen_sessions.jpg")
```


```{r}
#Plot app values and info session clicks over entire year
goal3_seventeen <- ggplot() +
  geom_line(data = seventeen_eighteen_app_values, 
           aes(x = date, y = goal3Completions, colour = "blue")) +
  geom_line(data = seventeen_eighteen_app_values, 
           aes(x = date, y = goal5Completions, colour = "red")) +
  labs(x = "Date", y = "Info Session Clicks") +
  theme(legend.position = "none")

goal9_seventeen<- 
  ggplot()+
  geom_line(data = seventeen_eighteen_app_values, 
           aes(x = date, y = goal9Completions)) +
  labs(x = "Date", y = "Applications")

ggarrange(goal3_seventeen, goal9_seventeen,
          ncol = 1, nrow = 2)
ggsave("seventeen_goal35.jpg")
```

```{r}
#Plot app values  and info packs over entire year
info_packs_seventeen_plot <- ggplot() +
  geom_line(data = seventeen_eighteen_app_values, 
           aes(x = date, y = goal12Completions, colour = "blue")) +
  labs(x = "Date", y = "Info Pack Requests") +
   theme(legend.position = "none")

ggarrange(info_packs_seventeen_plot, goal9_seventeen,
          ncol = 1, nrow = 2)
ggsave("seventeen_goal12.jpg")
```

```{r}
#Get correlations between metrics and app values
cor(seventeen_eighteen_app_values$goal9Completions, seventeen_eighteen_app_values$sessions)
cor(seventeen_eighteen_app_values$goal9Completions, seventeen_eighteen_app_values$goal3Completions)
cor(seventeen_eighteen_app_values$goal9Completions, seventeen_eighteen_app_values$goal5Completions)
cor(seventeen_eighteen_app_values$goal9Completions, seventeen_eighteen_app_values$goal12Completions)
```
Strongest correlation between sessions and app values but weak - info packs not
really correlated, too weak


```{r}
#Get data for FY2018/2019
eighteen_nineteen_app_values <-  read_csv("../inputs/eighteen_nineteen_apps.csv")
```

```{r}
summary(eighteen_nineteen_app_values)
```

```{r}
#Create data frame with both year's data
all_years_app_values <- 
rbind(seventeen_eighteen_app_values, eighteen_nineteen_app_values)

all_years_app_values <- 
all_years_app_values %>%
  mutate(FY = case_when(
    month %in% c("04", "05", "06", "07", "08", "09", "10", "11", "12") & year == "2017" ~ "2017/18",
    month %in% c("01", "02", "03") & year == "2018" ~ "2017/18",
    month %in% c("04", "05", "06", "07", "08", "09", "10", "11", "12") & year == "2018" ~ "2018/19",
    month %in% c("01", "02", "03") & year == "2019" ~ "2018/19",
  ))
```

```{r}
sum(all_years_app_values$goal9Completions)
```



```{r}
#Plot app values for each month and compare to last year
all_years_app_values %>%
  group_by(month, FY) %>%
  summarise(total_apps = sum(goal9Completions)) %>%
  ggplot() +
  geom_col(aes(x = month, y = total_apps, fill = FY), position = "dodge") +
  scale_fill_manual(values = c("violetred3", "springgreen3")) +
  geom_hline(yintercept = 45) +
  labs(title = "Applications per Month", x = "Month", y = "Number of Applications") +
  theme(title = element_text(size = 12, face = "bold"),
                  axis.title = element_text(size = 12, face = "bold"),
                  axis.text = element_text(size = 10),
                  legend.title = element_text(size = 10))
ggsave("apps_month.jpg")
```

```{r}
#Plot apps by day of the week and compare to last year
all_years_app_values %>%
  group_by(dayofWeekName, FY) %>%
  summarise(total_apps = sum(goal9Completions)) %>%
  ggplot() +
  geom_col(aes(x = reorder(dayofWeekName, total_apps), y = total_apps, fill = FY), position = "dodge") +
  scale_fill_manual(values = c("violetred3", "springgreen3")) +
  labs(title = "Applications per Week Day", x = "Day", y = "Number of Applications") +
  theme(title = element_text(size = 12, face = "bold"),
                  axis.title = element_text(size = 12, face = "bold"),
                  axis.text = element_text(size = 10),
                  legend.title = element_text(size = 10))
ggsave("apps_day.jpg")
```

```{r}
sum(seventeen_eighteen_app_values$goal9Completions)
sum(eighteen_nineteen_app_values$goal9Completions)
```

```{r}
#Plot apps by day of the month and compare to last year
all_years_app_values %>%
  group_by(day, FY) %>%
  summarise(total_apps = sum(goal9Completions)) %>%
  ggplot() +
  geom_col(aes(x = day, y = total_apps, fill = FY), position = "dodge") +
  scale_fill_manual(values = c("violetred3", "springgreen3")) +
  labs(title = "Applications per Month Day", x = "Day of Month", y = "Number of Applications") +
  theme(title = element_text(size = 12, face = "bold"),
                  axis.title = element_text(size = 12, face = "bold"),
                  axis.text = element_text(size = 10),
                  legend.title = element_text(size = 10))
ggsave("apps_month_day.jpg")
```

```{r}
#Plot sessions vs apps for FY2018/19
app_eighteen_plot <- ggplot() +
  geom_line(data = eighteen_nineteen_app_values, 
           aes(x = date, y = goal9Completions))  +
  labs(x = "Date", y = "Applications")

sessions_eighteen_plot <- ggplot() +
  geom_line(data = eighteen_nineteen_app_values, 
           aes(x = date, y = sessions)) +
  labs(x = "Date", y = "Sessions")

ggarrange(sessions_eighteen_plot, app_eighteen_plot,
          ncol = 1, nrow = 2)
ggsave("eighteen_sessions.jpg")
```

```{r}
#Plot app values and info session clicks over entire year
info_clicks_eighteen_plot <- ggplot() +
  geom_line(data = eighteen_nineteen_app_values, 
           aes(x = date, y = goal3Completions, colour = "blue")) +
  geom_line(data = eighteen_nineteen_app_values, 
           aes(x = date, y = goal5Completions, colour = "red")) +
  labs(x = "Date", y = "Info Session Clicks") +
  theme(legend.position = "none")

goal9_eighteen_plot <- 
ggplot() +
   geom_line(data = eighteen_nineteen_app_values, 
           aes(x = date, y = goal9Completions)) +
labs(x = "Date", y = "Applications")

ggarrange(info_clicks_eighteen_plot, goal9_eighteen_plot,
          ncol = 1, nrow = 2)
ggsave("eighteen_goal35.jpg")
```

```{r}
#Plot app values  and info packs over entire year
info_packs_eighteen_plot <- ggplot() +
  geom_line(data = eighteen_nineteen_app_values, 
           aes(x = date, y = goal12Completions, colour = "red")) +
  labs(x = "Date", y = "Info Pack Requests") +
  theme(legend.position = "none")

ggarrange(info_packs_eighteen_plot, goal9_eighteen_plot,
          ncol = 1, nrow = 2)

ggsave("eighteen_goal12.jpg")
```

```{r}
#Get correlations between metrics and app values
cor(eighteen_nineteen_app_values$goal9Completions, eighteen_nineteen_app_values$sessions)
cor(eighteen_nineteen_app_values$goal9Completions, eighteen_nineteen_app_values$goal3Completions)
cor(eighteen_nineteen_app_values$goal9Completions, eighteen_nineteen_app_values$goal5Completions)
cor(eighteen_nineteen_app_values$goal9Completions, eighteen_nineteen_app_values$goal12Completions)
```

```{r}
#Compare applications for each year
ggplot() +
  geom_line(data = seventeen_eighteen_app_values, aes(x = date, y = goal9Completions), colour = "violetred3") +
  geom_line(data = eighteen_nineteen_app_values, aes(x = date, y = goal9Completions), colour = "springgreen3") +
  labs(x = "Date", y = "Number of Applications") +
  theme(title = element_text(size = 12, face = "bold"),
                  axis.title = element_text(size = 12, face = "bold"),
                  axis.text = element_text(size = 10),
                  legend.title = element_text(size = 10))
ggsave("apps_2years.jpg")
```


```{r}
#Compare sessions for each year
ggplot() +
  geom_line(data = seventeen_eighteen_app_values, aes(x = date, y = sessions), colour = "violetred3") +
  geom_line(data = eighteen_nineteen_app_values, aes(x = date, y = sessions), colour = "springgreen3") +
  labs(x = "Date", y = "Number of Sessions") +
  theme(title = element_text(size = 12, face = "bold"),
                  axis.title = element_text(size = 12, face = "bold"),
                  axis.text = element_text(size = 10),
                  legend.title = element_text(size = 10))
ggsave("session_2years.jpg")
```

```{r}
#Compare info clicks for each year
ggplot() +
  geom_line(data = seventeen_eighteen_app_values, aes(x = date, y = goal3Completions), colour = "red") +
  geom_line(data = seventeen_eighteen_app_values, aes(x = date, y = goal5Completions), colour = "blue") +
  geom_line(data = eighteen_nineteen_app_values, aes(x = date, y = goal3Completions), colour = "yellow") +
  geom_line(data = eighteen_nineteen_app_values, aes(x = date, y = goal5Completions), colour = "green") +
  labs(x = "Date", y = "Number of Info Session Clicks") +
  theme(title = element_text(size = 12, face = "bold"),
                  axis.title = element_text(size = 12, face = "bold"),
                  axis.text = element_text(size = 10),
                  legend.title = element_text(size = 10))
ggsave("goal35_2years.jpg")
```

```{r}
#Compare info packs by year
ggplot() +
  geom_line(data = seventeen_eighteen_app_values, aes(x = date, y = goal12Completions), colour = "violetred3") +
  geom_line(data = eighteen_nineteen_app_values, aes(x = date, y = goal12Completions), colour = "springgreen3") +
  labs(x = "Date", y = "Number of Info Pack Requests") +
  theme(title = element_text(size = 12, face = "bold"),
                  axis.title = element_text(size = 12, face = "bold"),
                  axis.text = element_text(size = 10),
                  legend.title = element_text(size = 10))
ggsave("goal12_2years.jpg")
```

```{r}
#Plot apps for 2017/18
all_years_app_values %>%
  filter(FY == "2017/18")  %>%
  ggplot() +
  aes(x = date, y = goal9Completions) +
  geom_line()
```

```{r}
#Plot apps for 2018/19
all_years_app_values %>%
  filter(FY == "2018/19")  %>%
  ggplot() +
  aes(x = date, y = goal9Completions) +
  geom_line()
```






















